Natural Language Processing (NLP)
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
The field of AI that gives computers the ability to read, interpret, and generate human language, from machine translation to chatbots and search.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) is the field of AI that gets computers to read, interpret, and generate human language.
Language is ambiguous, contextual, and full of exceptions; computers run on numbers and exact rules. NLP is the set of techniques for crossing that gap, and it sits behind machine translation, chatbots, voice assistants, and modern search.
How NLP Works
Every NLP system starts by turning text into numbers. Tokenization splits the text into units a model can handle, and word embeddings map each unit to a vector that encodes its meaning.
A model then computes on those vectors. Early NLP relied on hand-written grammar rules; the field now runs on neural networks trained on large text corpora, most of them transformers built around the attention mechanism.
The output depends on the task: a label for a piece of text, a translation, an answer to a question, or the next word of a reply, generated one token at a time. Systems like ChatGPT are NLP end to end – here is how that pipeline works.
Applications of NLP
- Machine translation: Systems like Google Translate convert text between languages by learning from millions of existing translations.
- Sentiment analysis: Classifiers read reviews, support tickets, and social posts and label the opinion they express.
- Chatbots and voice assistants: Systems that parse a spoken or written request, work out the intent, and answer in natural language.
- Content moderation: Platforms run text classifiers over user posts to flag harassment, scams, and other policy violations at a scale no human team could read.
Example of NLP
A support chatbot receives: “Where’s my order? I paid two weeks ago.” A tokenizer splits the message into tokens, and an embedding step turns each token into a vector.
A classifier reads those vectors and labels the intent: delivery status inquiry, with a payment reference and a note of frustration. The system looks up the order and generates a reply in plain English, escalating to a human only if the customer pushes back.
Every stage of that exchange is NLP: converting text to numbers, extracting meaning, and producing language a person understands.
Related AI terms: Tokenization · Word Embedding · Attention Mechanism · Sentiment Analysis · Chatbot
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About